Typical real estate underwriting procedures require three credit scores for assessing a consumer's credit worthiness, one score from each of the three credit reporting companies (CRCs). Lenders require that these scores are not only predictive of credit risk but also are highly consistent in their absolute value. Scoring algorithms that provide inconsistent scores can increase the risk exposure that a lender takes on and offers the borrower less attractive products and pricing.
Inconsistent scores occur largely due to score algorithm differences among CRCs, timing submission and content variations in data reported by creditors. A credit score for a consumer can vary by more than 60 points between the CRCs.
Measuring score consistency is challenging for the reasons stated previously and additionally due to the fact that scores often use different ranges. Thus, for example, a consumer may score 650 using two different algorithms yet have very different risk profiles. It is possible that the former algorithm has a range of 300 to 700 where 650 indicates low risk and the latter algorithm has a range of 600 to 900 where 650 indicates high risk.
As lenders look to improve the quality of their underwriting processes, a framework is necessary for evaluating the consistency of generic risk score algorithms.